langchain

Commit Graph

Author	SHA1	Message	Date
Bagatur	493e474063	docs: udpated api reference (#25172 ) - Move the API reference into the vercel build - Update api reference organization and styling	1 month ago
ccurme	27690506d0	multiple: update removal targets (#25361 )	1 month ago
Eugene Yurtsev	86355640c3	experimental[patch]: Use get_fields adapter (#25193 ) Change all usages of __fields__ with get_fields adapter merged into langchain_core. Code mod generated using the following grit pattern: ``` engine marzano(0.1) language python `$X.__fields__` => `get_fields($X)` where { add_import(source="langchain_core.utils.pydantic", name="get_fields") } ```	1 month ago
Eugene Yurtsev	b9f65e5038	experimental[patch]: Migrate pydantic extra to literals (#25194 ) Migrate pydantic extra to literals Upgrade to using a literal for specifying the extra which is the recommended approach in pydantic 2. This works correctly also in pydantic v1. ```python from pydantic.v1 import BaseModel class Foo(BaseModel, extra="forbid"): x: int Foo(x=5, y=1) ``` And ```python from pydantic.v1 import BaseModel class Foo(BaseModel): x: int class Config: extra = "forbid" Foo(x=5, y=1) ``` ## Enum -> literal using grit pattern: ``` engine marzano(0.1) language python or { `extra=Extra.allow` => `extra="allow"`, `extra=Extra.forbid` => `extra="forbid"`, `extra=Extra.ignore` => `extra="ignore"` } ``` Resorted attributes in config and removed doc-string in case we will need to deal with going back and forth between pydantic v1 and v2 during the 0.3 release. (This will reduce merge conflicts.) ## Sort attributes in Config: ``` engine marzano(0.1) language python function sort($values) js { return $values.text.split(',').sort().join("\n"); } class_definition($name, $body) as $C where { $name <: `Config`, $body <: block($statements), $values = [], $statements <: some bubble($values) assignment() as $A where { $values += $A }, $body => sort($values), } ```	1 month ago
Tomaz Bratanic	d166967003	experimental: Add gliner graph transformer (#25066 ) You can use this with: ``` from langchain_experimental.graph_transformers import GlinerGraphTransformer gliner = GlinerGraphTransformer(allowed_nodes=["Person", "Organization", "Nobel"], allowed_relationships=["EMPLOYEE", "WON"]) from langchain_core.documents import Document text = """ Marie Curie, was a Polish and naturalised-French physicist and chemist who conducted pioneering research on radioactivity. She was the first woman to win a Nobel Prize, the first person to win a Nobel Prize twice, and the only person to win a Nobel Prize in two scientific fields. Her husband, Pierre Curie, was a co-winner of her first Nobel Prize, making them the first-ever married couple to win the Nobel Prize and launching the Curie family legacy of five Nobel Prizes. She was, in 1906, the first woman to become a professor at the University of Paris. """ documents = [Document(page_content=text)] gliner.convert_to_graph_documents(documents) ``` --------- Co-authored-by: Erick Friis <erick@langchain.dev>	2 months ago
Tomaz Bratanic	f9a11a9197	Add relik transformer config (#25019 )	2 months ago
Bagatur	e81ddb32a6	docs: fix kwargs docstring (#25010 ) Fix: ![Screenshot 2024-08-02 at 5 33 37 PM](https://github.com/user-attachments/assets/7c56cdeb-ee81-454c-b3eb-86aa8a9bdc8d)	2 months ago
ccurme	c2538e7834	experimental[patch]: bump min versions of core and community (#24996 ) Ollama functions unit test broken with min version of community.	2 months ago
ccurme	4797b806c2	experimental[patch]: release 0.0.64 (#24990 )	2 months ago
Tomaz Bratanic	7061869aec	Add relik graph transformer (#24982 ) Relik is a new library for graph extraction that offers smaller and cheaper models for graph construction	2 months ago
cffranco94	b01d938997	experimental: Add config to convert_to_graph_documents (#24012 ) PR title: Experimental: Add config to convert_to_graph_documents Description: In order to use langfuse, i need to pass the langfuse configuration when invoking the chain. langchain_experimental does not allow to add any parameters (beside the documents) to the convert_to_graph_documents method. This way, I cannot monitor the chain in langfuse. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. --------- Co-authored-by: Catarina Franco <catarina.franco@criticalsoftware.com> Co-authored-by: Chester Curme <chester.curme@gmail.com>	2 months ago
ZhangShenao	4a05679fdb	patch[experimental] Fix prompt in `GenerativeAgentMemory` (#24771 ) There is an issue with the prompt format in `GenerativeAgentMemory` , try to fix it. The prompt is same as the one in method `_score_memory_importance`.	2 months ago
WU LIFU	2ba8393182	graph_transformers: bug fix for create_simple_model not passing in ll… (#24643 ) issue: #24615 descriptions: The _Graph pydantic model generated from create_simple_model (which LLMGraphTransformer uses when allowed nodes and relationships are provided) does not constrain the relationships (source and target types, relationship type), and the node and relationship properties with enums when using ChatOpenAI. The issue is that when calling optional_enum_field throughout create_simple_model the llm_type parameter is not passed in except for when creating node type. Passing it into each call fixes the issue. Co-authored-by: Lifu Wu <lifu@nextbillion.ai>	2 months ago
Eugene Yurtsev	c623ae6661	experimental[patch]: Fix import test (#24672 ) Import test was misconfigured, the glob wasn't returning any file paths	2 months ago
Isaac Francisco	5c7e589aaf	deprecating ollama_functions (#24632 )	2 months ago
Eugene Yurtsev	89bcca3542	experimental[patch]: Bump core (#24671 )	2 months ago
Joel Akeret	acfce30017	Adding compatibility for OllamaFunctions with ImagePromptTemplate (#24499 ) - [ ] PR title: "experimental: Adding compatibility for OllamaFunctions with ImagePromptTemplate" - [ ] PR message: - Description: Removes the outdated `_convert_messages_to_ollama_messages` method override in the `OllamaFunctions` class to ensure that ollama multimodal models can be invoked with an image. - Issue: #24174 --------- Co-authored-by: Joel Akeret <joel.akeret@ti&m.com> Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com> Co-authored-by: isaac hershenson <ihershenson@hmc.edu>	2 months ago
Bagatur	9b7db08184	experimental[patch]: Release 0.0.63 (#24563 )	2 months ago
Erick Friis	3dce2e1d35	all: add release notes to pypi (#24519 )	2 months ago
Bagatur	236e957abb	core,groq,openai,mistralai,robocorp,fireworks,anthropic[patch]: Update BaseModel subclass and instance checks to handle both v1 and proper namespaces (#24417 ) After this PR chat models will correctly handle pydantic 2 with bind_tools and with_structured_output. ```python import pydantic print(pydantic.__version__) ``` 2.8.2 ```python from langchain_openai import ChatOpenAI from pydantic import BaseModel, Field class Add(BaseModel): x: int y: int model = ChatOpenAI().bind_tools([Add]) print(model.invoke('2 + 5').tool_calls) model = ChatOpenAI().with_structured_output(Add) print(type(model.invoke('2 + 5'))) ``` ``` [{'name': 'Add', 'args': {'x': 2, 'y': 5}, 'id': 'call_PNUFa4pdfNOYXxIMHc6ps2Do', 'type': 'tool_call'}] <class '__main__.Add'> ``` ```python from langchain_openai import ChatOpenAI from pydantic.v1 import BaseModel, Field class Add(BaseModel): x: int y: int model = ChatOpenAI().bind_tools([Add]) print(model.invoke('2 + 5').tool_calls) model = ChatOpenAI().with_structured_output(Add) print(type(model.invoke('2 + 5'))) ``` ```python [{'name': 'Add', 'args': {'x': 2, 'y': 5}, 'id': 'call_hhiHYP441cp14TtrHKx3Upg0', 'type': 'tool_call'}] <class '__main__.Add'> ``` Addresses issues: https://github.com/langchain-ai/langchain/issues/22782 --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	2 months ago
Eugene Yurtsev	6182a402f1	experimental[patch]: block a few more things from PALValidator (#24379 ) * Please see security warning already in existing class. * The approach here is fundamentally insecure as it's relying on a block approach rather than an approach based on only running allowed nodes. So users should only use this code if its running from a properly sandboxed environment.	2 months ago
William FH	c5a07e2dd8	core[patch]: add InjectedToolArg annotation (#24279 ) ```python from typing_extensions import Annotated from langchain_core.tools import tool, InjectedToolArg from langchain_anthropic import ChatAnthropic @tool def multiply(x: int, y: int, not_for_model: Annotated[dict, InjectedToolArg]) -> str: """multiply.""" return x * y ChatAnthropic(model='claude-3-sonnet-20240229',).bind_tools([multiply]).invoke('5 times 3').tool_calls ''' -> [{'name': 'multiply', 'args': {'x': 5, 'y': 3}, 'id': 'toolu_01Y1QazYWhu4R8vF4hF4z9no', 'type': 'tool_call'}] ''' ``` --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	2 months ago
Erick Friis	007c5a85d5	multiple: use modern installer in poetry (#23998 )	2 months ago
Bagatur	a0c2281540	infra: update mypy 1.10, ruff 0.5 (#23721 ) ```python """python scripts/update_mypy_ruff.py""" import glob import tomllib from pathlib import Path import toml import subprocess import re ROOT_DIR = Path(__file__).parents[1] def main(): for path in glob.glob(str(ROOT_DIR / "libs/*/pyproject.toml"), recursive=True): print(path) with open(path, "rb") as f: pyproject = tomllib.load(f) try: pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = ( "^1.10" ) pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = ( "^0.5" ) except KeyError: continue with open(path, "w") as f: toml.dump(pyproject, f) cwd = "/".join(path.split("/")[:-1]) completed = subprocess.run( "poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color", cwd=cwd, shell=True, capture_output=True, text=True, ) logs = completed.stdout.split("\n") to_ignore = {} for l in logs: if re.match("^(.)\:(\d+)\: error:.\[(.)\]", l): path, line_no, error_type = re.match( "^(.)\:(\d+)\: error:.\[(.*)\]", l ).groups() if (path, line_no) in to_ignore: to_ignore[(path, line_no)].append(error_type) else: to_ignore[(path, line_no)] = [error_type] print(len(to_ignore)) for (error_path, line_no), error_types in to_ignore.items(): all_errors = ", ".join(error_types) full_path = f"{cwd}/{error_path}" try: with open(full_path, "r") as f: file_lines = f.readlines() except FileNotFoundError: continue file_lines[int(line_no) - 1] = ( file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n" ) with open(full_path, "w") as f: f.write("".join(file_lines)) subprocess.run( "poetry run ruff format .; poetry run ruff --select I --fix .", cwd=cwd, shell=True, capture_output=True, text=True, ) if __name__ == "__main__": main() ```	3 months ago
Jordy Jackson Antunes da Rocha	a50eabbd48	experimental: LLMGraphTransformer add missing conditional adding restrictions to prompts for LLM that do not support function calling (#22793 ) - Description: Modified the prompt created by the function `create_unstructured_prompt` (which is called for LLMs that do not support function calling) by adding conditional checks that verify if restrictions on entity types and rel_types should be added to the prompt. If the user provides a sufficiently large text, the current prompt may fail to produce results in some LLMs. I have first seen this issue when I implemented a custom LLM class that did not support Function Calling and used Gemini 1.5 Pro, but I was able to replicate this issue using OpenAI models. By loading a sufficiently large text ```python from langchain_community.llms import Ollama from langchain_openai import ChatOpenAI, OpenAI from langchain_core.prompts import PromptTemplate import re from langchain_experimental.graph_transformers import LLMGraphTransformer from langchain_core.documents import Document with open("texto-longo.txt", "r") as file: full_text = file.read() partial_text = full_text[:4000] documents = [Document(page_content=partial_text)] # cropped to fit GPT 3.5 context window ``` And using the chat class (that has function calling) ```python chat_openai = ChatOpenAI(model="gpt-3.5-turbo", model_kwargs={"seed": 42}) chat_gpt35_transformer = LLMGraphTransformer(llm=chat_openai) graph_from_chat_gpt35 = chat_gpt35_transformer.convert_to_graph_documents(documents) ``` It works: ``` >>> print(graph_from_chat_gpt35[0].nodes) [Node(id="Jesu, Joy of Man's Desiring", type='Music'), Node(id='Godel', type='Person'), Node(id='Johann Sebastian Bach', type='Person'), Node(id='clever way of encoding the complicated expressions as numbers', type='Concept')] ``` But if you try to use the non-chat LLM class (that does not support function calling) ```python openai = OpenAI( model="gpt-3.5-turbo-instruct", max_tokens=1000, ) gpt35_transformer = LLMGraphTransformer(llm=openai) graph_from_gpt35 = gpt35_transformer.convert_to_graph_documents(documents) ``` It uses the prompt that has issues and sometimes does not produce any result ``` >>> print(graph_from_gpt35[0].nodes) [] ``` After implementing the changes, I was able to use both classes more consistently: ```shell >>> chat_gpt35_transformer = LLMGraphTransformer(llm=chat_openai) >>> graph_from_chat_gpt35 = chat_gpt35_transformer.convert_to_graph_documents(documents) >>> print(graph_from_chat_gpt35[0].nodes) [Node(id="Jesu, Joy Of Man'S Desiring", type='Music'), Node(id='Johann Sebastian Bach', type='Person'), Node(id='Godel', type='Person')] >>> gpt35_transformer = LLMGraphTransformer(llm=openai) >>> graph_from_gpt35 = gpt35_transformer.convert_to_graph_documents(documents) >>> print(graph_from_gpt35[0].nodes) [Node(id='I', type='Pronoun'), Node(id="JESU, JOY OF MAN'S DESIRING", type='Song'), Node(id='larger memory', type='Memory'), Node(id='this nice tree structure', type='Structure'), Node(id='how you can do it all with the numbers', type='Process'), Node(id='JOHANN SEBASTIAN BACH', type='Composer'), Node(id='type of structure', type='Characteristic'), Node(id='that', type='Pronoun'), Node(id='we', type='Pronoun'), Node(id='worry', type='Verb')] ``` The results are a little inconsistent because the GPT 3.5 model may produce incomplete json due to the token limit, but that could be solved (or mitigated) by checking for a complete json when parsing it.	3 months ago
ccurme	62b16fcc6b	experimental: release 0.0.62 (#23507 )	3 months ago
Tomaz Bratanic	22fa32e164	LLM Graph transformer dealing with empty strings (#23368 ) Pydantic allows empty strings: ``` from langchain.pydantic_v1 import Field, BaseModel class Property(BaseModel): """A single property consisting of key and value""" key: str = Field(..., description="key") value: str = Field(..., description="value") x = Property(key="", value="") ``` Which can produce errors downstream. We simply ignore those records	3 months ago
Brace Sproul	abe7566d7d	core[minor]: BaseChatModel with_structured_output implementation (#22859 )	3 months ago
Raviraj	858ce264ef	SemanticChunker : Feature Addition ("Semantic Splitting with gradient") (#22895 ) ```SemanticChunker``` currently provide three methods to split the texts semantically: - percentile - standard_deviation - interquartile I propose new method ```gradient```. In this method, the gradient of distance is used to split chunks along with the percentile method (technically) . This method is useful when chunks are highly correlated with each other or specific to a domain e.g. legal or medical. The idea is to apply anomaly detection on gradient array so that the distribution become wider and easy to identify boundaries in highly semantic data. I have tested this merge on a set of 10 domain specific documents (mostly legal). Details : - Issue: Improvement - Dependencies: NA - Twitter handle: [x.com/prajapat_ravi](https://x.com/prajapat_ravi) @hwchase17 --------- Co-authored-by: Raviraj Prajapat <raviraj.prajapat@sirionlabs.com> Co-authored-by: isaac hershenson <ihershenson@hmc.edu>	3 months ago
Tomaz Bratanic	1c661fd849	Improve llm graph transformer docstring (#22939 )	3 months ago
Erick Friis	c374c98389	experimental: release 0.0.61 (#22924 )	3 months ago
Istvan/Nebulinq	513e491ce9	experimental: LLMGraphTransformer - added relationship properties. (#21856 ) - Description: The generated relationships in the graph had no properties, but the Relationship class was properly defined with properties. This made it very difficult to transform conditional sentences into a graph. Adding properties to relationships can solve this issue elegantly. The changes expand on the existing LLMGraphTransformer implementation but add the possibility to define allowed relationship properties like this: LLMGraphTransformer(llm=llm, relationship_properties=["Condition", "Time"],) - Issue: no issue found - Dependencies: n/a - Twitter handle: @IstvanSpace -Quick Test ================================================================= from dotenv import load_dotenv import os from langchain_community.graphs import Neo4jGraph from langchain_experimental.graph_transformers import LLMGraphTransformer from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.documents import Document load_dotenv() os.environ["NEO4J_URI"] = os.getenv("NEO4J_URI") os.environ["NEO4J_USERNAME"] = os.getenv("NEO4J_USERNAME") os.environ["NEO4J_PASSWORD"] = os.getenv("NEO4J_PASSWORD") graph = Neo4jGraph() llm = ChatOpenAI(temperature=0, model_name="gpt-4o") llm_transformer = LLMGraphTransformer(llm=llm) #text = "Harry potter likes pies, but only if it rains outside" text = "Jack has a dog named Max. Jack only walks Max if it is sunny outside." documents = [Document(page_content=text)] llm_transformer_props = LLMGraphTransformer( llm=llm, relationship_properties=["Condition"], ) graph_documents_props = llm_transformer_props.convert_to_graph_documents(documents) print(f"Nodes:{graph_documents_props[0].nodes}") print(f"Relationships:{graph_documents_props[0].relationships}") graph.add_graph_documents(graph_documents_props) --------- Co-authored-by: Istvan Lorincz <istvan.lorincz@pm.me> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	3 months ago
Eugene Yurtsev	c72bcda4f2	community[major], experimental[patch]: Remove Python REPL from community (#22904 ) Remove the REPL from community, and suggest an alternative import from langchain_experimental. Fix for this issue: https://github.com/langchain-ai/langchain/issues/14345 This is not a bug in the code or an actual security risk. The python REPL itself is behaving as expected. The PR is done to appease blanket security policies that are just looking for the presence of exec in the code. --------- Co-authored-by: Erick Friis <erick@langchain.dev>	3 months ago
Kagura Chen	57783c5e55	Fix: lint errors and update Field alias in models.py and AutoSelectionScorer initialization (#22846 ) This PR addresses several lint errors in the core package of LangChain. Specifically, the following issues were fixed: 1.Unexpected keyword argument "required" for "Field" [call-arg] 2.tests/integration_tests/chains/test_cpal.py:263: error: Unexpected keyword argument "narrative_input" for "QueryModel" [call-arg]	3 months ago
Eugene Yurtsev	a766815a99	experimental[patch]/docs[patch]: Update links to security docs (#22864 ) Minor update to newest version of security docs (content should be identical).	3 months ago
Eugene Yurtsev	ce0b0f22a1	experimental[major]: Force users to opt-in into code that relies on the python repl (#22860 ) This should make it obvious that a few of the agents in langchain experimental rely on the python REPL as a tool under the hood, and will force users to opt-in.	3 months ago
Karim Lalani	276be6cdd4	[experimental][llms][OllamaFunctions] tool calling related fixes (#22339 ) Fixes issues with tool calling to handle tool objects correctly. Added support to handle ToolMessage correctly. Added additional checks for error conditions. --------- Co-authored-by: ccurme <chester.curme@gmail.com>	3 months ago
Christophe Bornet	d04e899b56	ci: add testing with Python 3.12 (#22813 ) We need to use a different version of numpy for py3.8 and py3.12 in pyproject. And so do projects that use that Python version range and import langchain. - Twitter handle: _cbornet	3 months ago
Erick Friis	a24a9c6427	multiple: get rid of pyproject extras (#22581 ) They cause `poetry lock` to take a ton of time, and `uv pip install` can resolve the constraints from these toml files in trivial time (addressing problem with #19153) This allows us to properly upgrade lockfile dependencies moving forward, which revealed some issues that were either fixed or type-ignored (see file comments)	4 months ago
Mohammad Mohtashim	7fcef2556c	[Experimental]: Async agenerate method ollama functions (#21682 ) - Description: : Added Async method for Generate for OllamaFunctions which was missing and was raising errors for the users. - Issue: #21422	4 months ago
Bagatur	48fba40fce	experimental[patch]: Release 0.0.60 (#22497 )	4 months ago
liugz18	8fd231086e	experimental[patch]: Fix graph_transformers llms #21482 (#22417 ) Fix AttributeError on calling LLMGraphTransformer.convert_to_graph_documents #21482 since raw_schema is always a str @baskaryan	4 months ago
Karim Lalani	a1899439fc	[experimental][llms][ollama_functions] Update OllamaFunctions to send `tool_calls` attribute (#21625 ) Update OllamaFunctions to return `tool_calls` for AIMessages when used for tool calling.	4 months ago
Bagatur	50186da0a1	infra: rm unused # noqa violations (#22049 ) Updating #21137	4 months ago
mochi	63284ffebf	experimental[patch], docs: refine notebook for MyScale `SelfQueryRetriever` (#22016 ) - Description: upgrade model to `gpt-4o`	4 months ago
Tomaz Bratanic	a43515ca65	experimental[patch]: Pass enum only to openai in llm graph transformer (#21860 ) Some models like Groq return bad request if you pass in `enum` parameter in tool definition	4 months ago
Erick Friis	2d3f4e1a16	experimental: release 0.0.59 (#21835 )	4 months ago
Erick Friis	c77d2f2b06	multiple: core 0.2 nonbreaking dep, check_diff community->langchain dep (#21646 ) 0.2 is not a breaking release for core (but it is for langchain and community) To keep the core+langchain+community packages in sync at 0.2, we will relax deps throughout the ecosystem to tolerate `langchain-core` 0.2	4 months ago
Tomaz Bratanic	89ff6a3d3b	Add sentiment and confidence levels to diffbotgraphtransformer (#21590 ) Co-authored-by: Erick Friis <erickfriis@gmail.com> Co-authored-by: Erick Friis <erick@langchain.dev>	4 months ago
Erick Friis	83eecd54fe	experimental: 0.2 relax (#21468 )	4 months ago

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313 Commits (f0f125dac7f58528d02efd248ba7f2aaf6af7c05)