langchain

Commit Graph

Author	SHA1	Message	Date
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
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
Tomaz Bratanic	1c661fd849	Improve llm graph transformer docstring (#22939 )	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
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
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
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
Tomaz Bratanic	0bf7596839	Add simple node properties to llm graph transformer (#21369 ) Add support for simple node properties in llm graph transformer. Linter and dynamic pydantic classes aren't friends, hence I added two ignores	5 months ago
Tomaz Bratanic	ad3fd44a7f	experimental: Fix llm graph transformer bug (#21362 )	5 months ago
Tomaz Bratanic	5b6d1a907d	Add the extract types to diffbot graph transformer (#21315 ) Before you could only extract triples (diffbot calls it facts) from diffbot to avoid isolated nodes. However, sometimes isolated nodes can still be useful like for prefiltering, so we want to allow users to extract them if they want. Default behaviour is unchanged.	5 months ago
Tomaz Bratanic	7860e4c649	experimental[patch]: Add support for non-function calling LLMs in llm graph transformers (#21014 )	5 months ago
Tomaz Bratanic	a1b105ac00	experimental[patch]: Skip pydantic validation for llm graph transformer and fix JSON response where possible (#19915 ) LLMs might sometimes return invalid response for LLM graph transformer. Instead of failing due to pydantic validation, we skip it and manually check and optionally fix error where we can, so that more information gets extracted	5 months ago
Luca Dorigo	f19229c564	core[patch]: fix beta, deprecated typing (#18877 ) Description: While not technically incorrect, the TypeVar used for the `@beta` decorator prevented pyright (and thus most vscode users) from correctly seeing the types of functions/classes decorated with `@beta`. This is in part due to a small bug in pyright (https://github.com/microsoft/pyright/issues/7448 ) - however, the `Type` bound in the typevar `C = TypeVar("C", Type, Callable)` is not doing anything - classes are `Callables` by default, so by my understanding binding to `Type` does not actually provide any more safety - the modified annotation still works correctly for both functions, properties, and classes. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	6 months ago
Tomaz Bratanic	b04e663426	experimental[patch]: Flatten relationships in LLM graph transformer (#19642 )	6 months ago
Juan Jose Miguel Ovalle Villamil	1fe10a3e3d	experimental[patch]: Enhance LLMGraphTransformer with async processing and improved readability (#19205 ) - [x] PR title: "experimental: Enhance LLMGraphTransformer with async processing and improved readability" - [x] PR message: - Description: This pull request refactors the `process_response` and `convert_to_graph_documents` methods in the LLMGraphTransformer class to improve code readability and adds async versions of these methods for concurrent processing. The main changes include: - Simplifying list comprehensions and conditional logic in the process_response method for better readability. - Adding async versions aprocess_response and aconvert_to_graph_documents to enable concurrent processing of documents. These enhancements aim to improve the overall efficiency and maintainability of the `LLMGraphTransformer` class. - Issue: N/A - Dependencies: No additional dependencies required. - Twitter handle: @jjovalle99 - [x] Add tests and docs: N/A (This PR does not introduce a new integration) - [x] Lint and test: Ran make format, make lint, and make test from the root of the modified package(s). All tests pass successfully. Additional notes: - The changes made in this PR are backwards compatible and do not introduce any breaking changes. - The PR touches only the `LLMGraphTransformer` class within the experimental package. --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	6 months ago
Leonid Ganeline	4159a4723c	experimental[patch]: update module doc strings (#19539 ) Added missed module descriptions. Fixed format.	6 months ago
Tomaz Bratanic	cda43c5a11	experimental[patch]: Fix LLM graph transformer default prompt (#18856 ) Some LLMs do not allow multiple user messages in sequence.	7 months ago
Tomaz Bratanic	246724faab	LLM graph transformer prompt engineering (#18843 ) A bit of prompt engineering to improve results	7 months ago
Tomaz Bratanic	c8c592d3f1	experimental[minor]: Add LLM graph transformer (#18733 ) Add a class that constructs knowledge graphs based on text using an LLM.	7 months ago
Tomaz Bratanic	010a234f1e	docs: Fix diffbot graph transformer description (#18736 ) The previous docstring was invalid	7 months ago
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
Bagatur	fa5d49f2c1	docs, experimental[patch], langchain[patch], community[patch]: update storage imports (#15429 ) ran ```bash g grep -l "langchain.vectorstores" \| xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g" g grep -l "langchain.document_loaders" \| xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g" g grep -l "langchain.chat_loaders" \| xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g" g grep -l "langchain.document_transformers" \| xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g" g grep -l "langchain\.graphs" \| xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g" g grep -l "langchain\.memory\.chat_message_histories" \| xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g" gco master libs/langchain/tests/unit_tests//test_imports.py gco master libs/langchain/tests/unit_tests/*/test_public_api.py ```	9 months ago
Tomaz Bratanic	db73c9d5b5	Diffbot Graph Transformer / Neo4j Graph document ingestion (#9979 ) Co-authored-by: Bagatur <baskaryan@gmail.com>	1 year ago

24 Commits (6cd56821dc0e0bf37b276a0de7717ee0c799e4af)