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Community : Modified doc strings and example notebook for Clarifai
Description:
1. Modified doc strings inside clarifai vectorstore class and
embeddings.
2. Modified notebook examples.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** Adds a RAG template that uses NVIDIA AI playground
and embedding models, along with Milvus vector store
- **Dependencies:** This template depends on the AI playground service
in NVIDIA NGC. API keys with a significant trial compute are available
(10k queries at the time of writing). This template also depends on the
Milvus Vector store which is publicly available.
Note: [A quick link to get a
key](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/codellama-13b/api)
when you have an NGC account. Generate Key button at the top right of
the code window.
---------
Co-authored-by: Sagar B Manjunath <sbogadimanju@nvidia.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR fixes an issue where AgentExecutor with RunnableAgent does not allow users to see individual llm tokens if streaming=True is not set explicitly on the underlying chat model.
The majority of this PR is testing code:
1. Create a test chat model that makes it easier to test streaming and
supports AIMessages that include function invocation information.
2. Tests for the chat model
3. Tests for RunnableAgent (previously untested)
4. Tests for openai agent (previously untested)
- **Description:**
`QianfanChatEndpoint` extends `BaseChatModel` as a super class, which
has a default stream implement might concat the MessageChunk with
`__add__`. When call stream(), a ValueError for duplicated key will be
raise.
- **Issues:**
* #13546
* #13548
* merge two single test file related to qianfan.
- **Dependencies:** no
- **Tag maintainer:**
---------
Co-authored-by: root <liujun45@baidu.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
experimental relies on `from langchain_core.runnables.config import
run_in_executor` which was introduced in core 0.1.5.
Updated pyproject dependency as well as minimum version test.
Now the SQL used to delete vector doc from myscale is as follow:
```sql
DELETE FROM collection WHERE id = '1' AND id = '2' AND id = '3'
```
But the expected one should be
```sql
DELETE FROM collection WHERE id IN ('1', '2', '3')
```
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**Description:** Fixes the word "iteratively" in the use-cases
documentation
**Twitter handle:** @untilhamza
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This change fixes the AstraDB logical operator filtering (`$and,`
`$or`).
The `metadata` prefix must not be added if the key is `$and` or `$or`.
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See preview :
https://langchain-git-fork-cbornet-astra-loader-doc-langchain.vercel.app/docs/integrations/document_loaders/astradb
This means that users of astream_log() now get streamed output of
virtually all requested runs, whereas before the only streamed output
would be for the root run and raw llm runs
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- **Description:** Add missing import of 'ConfigurableField' in 'Full
code comparison' example in LCEL
- **Issue:** Example code not running
- **Dependencies:** None
- **Twitter handle:** @heyyoshan
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- **Description:** This update rectifies an error in the notebook by
changing the input variable from `zhipu_api_key` to `api_key`. It also
includes revisions to comments to improve program readability.
- **Issue:** The input variable in the notebook example should be
`api_key` instead of `zhipu_api_key`.
- **Dependencies:** No additional dependencies are required for this
change.
To ensure quality and standards, we have performed extensive linting and
testing. Commands such as make format, make lint, and make test have
been run from the root of the modified package to ensure compliance with
LangChain's coding standards.
- ArgillaCallbackHandler does not properly set the default values while
initializing. This PR corrects the line.
- Issue: #15531
- Dependencies: Argilla
- Also corrected some dead links.
fix of #14905
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Improving documentation
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- **Description:** Adding resource for Curie model
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- **Twitter handle:** @mmarccode
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** The `delete_collection` method deletes an entire
collection regardless of custom ID. The `delete` method deletes
everything with the provided custom IDs regardless of collection. It can
be useful to restrict deletion to both the collection and a set of
custom IDs. This change adds support for that by allowing you to
optionally specify that `delete` should be restricted to the collection
defined on the `PGVector` instance.
- **Description:** Includes the PDF ID in the MathPix document metadata.
This is useful in case you need to re-request a processed PDF from the
MathPix API later.
- **Description:** The `error_info['id']` can be cross-referenced with
the MathPix API documentation to get very specific information about why
an error occurred.
- **Description:** This PR is to fix a bug of "system message check" in
langchain_community/ chat_models/tongyi.py
- **Issue:** In term of current logic, if there's no system message in
the chat messages, an error of "System message can only be the first
message." will be wrongly raised.
- **Dependencies:** No.
- **Twitter handle:** I don't have a Twitter account.
- **Description:** This PR is to fix a bug in
semantic_hybrid_search_with_score_and_rerank() function in
langchain_community/vectorstores/azuresearch.py. The hardcoded
"metadata" name is replaced with FIELDS_METADATA variable with an if
block to check if the metadata column exists or not.
- **Issue:** Fixed#15581
- **Dependencies:** No
- **Twitter handle:** None
Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>
Updates docs and cookbooks to import ChatOpenAI, OpenAI, and OpenAI
Embeddings from `langchain_openai`
There are likely more
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Todo
- [x] copy over integration tests
- [x] update docs with new instructions in #15513
- [x] add linear ticket to bump core -> community, community->langchain,
and core->openai deps
- [ ] (optional): add `pip install langchain-openai` command to each
notebook using it
- [x] Update docstrings to not need `openai` install
- [x] Add serialization
- [x] deprecate old models
Contributor steps:
- [x] Add secret names to manual integrations workflow in
.github/workflows/_integration_test.yml
- [x] Add secrets to release workflow (for pre-release testing) in
.github/workflows/_release.yml
Maintainer steps (Contributors should not do these):
- [x] set up pypi and test pypi projects
- [x] add credential secrets to Github Actions
- [ ] add package to conda-forge
Functional changes to existing classes:
- now relies on openai client v1 (1.6.1) via concrete dep in
langchain-openai package
Codebase organization
- some function calling stuff moved to
`langchain_core.utils.function_calling` in order to be used in both
community and langchain-openai
removed the deprecated model from text embedding page of openai notebook
and added the suggested model from openai page
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Removes unused `Params` in `libs/langchain/langchain/llms/mlflow.py`.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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The example code for `llms.Mlflow` is outdated.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** `MarkdownHeaderTextSplitter` currently strips header
lines from chunked content. Many applications require these header lines
are preserved. This adds an optional parameter to preserve those headers
in the chunked content.
- **Issue:** #2836 (relevant)
- **Dependencies:** -
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @finnless
Unit tests and new examples in notebook included.
cc @rlancemartin
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Adds `WasmChat` integration. `WasmChat` runs GGUF models locally or via
chat service in lightweight and secure WebAssembly containers. In this
PR, `WasmChatService` is introduced as the first step of the
integration. `WasmChatService` is driven by
[llama-api-server](https://github.com/second-state/llama-utils) and
[WasmEdge Runtime](https://wasmedge.org/).
---------
Signed-off-by: Xin Liu <sam@secondstate.io>
Follow up on https://github.com/langchain-ai/langchain/pull/13048.
This PR intends to simplify the Qdrant async implementation by replacing
the internal GRPC methods with the `QdrantAsyncClient` methods.
This is a backward compatible change with no additional steps required
after merge.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixes#14347
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- **Description:** Added the traceback of the previous error to keep the
initial error type,
- **Issue:** #14347 ,
- **Dependencies:** None,
- **Tag maintainer:**
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---------
Co-authored-by: Julien Raffy <julien.raffy@emeria.eu>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** the ability to add all extra parameter of vectorstore
and using them SemanticSimilarityExampleSelector.
- **Issue:** #14583
- **Dependencies:** no dependensies
- **Tag maintainer:**
- **Twitter handle:** @AmirMalekiz
---------
Co-authored-by: Amir Maleki <amaleki@fb.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description: Add support for setting the `score_threshold` for
similarity search in SupabaseVectoreStore.
This pull request addresses issue #14438
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** changed json.py to handle additional cases of partial
json string to be parsed, basically by dropping the last character in
the string until a valid json string is found or the string is empty.
Also added additional test cases.
- **Issue:** function parse_partial_json could not parse cases where the
key is present but the value is not.
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Because Milvus' collection_name doesn't support UFT8 characters in other
languages, I want the `collection_descriotion`.
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**Description:** Fix for processing for serpapi response for Google Maps
API
**Issue:** Due to the fact corresponding
[api](https://serpapi.com/google-maps-api) returns 'local_results' as
list, and old version requested `res["local_results"].keys()` of the
list. As the result we got exception: ```AttributeError: 'list' object
has no attribute 'keys'```.
Way to reproduce wrong behaviour:
```
params = {
"engine": "google_maps",
"type": "search",
"google_domain": "google.de",
"ll": "@51.1917,10.525,14z",
"hl": "de",
"gl": "de",
}
search = SerpAPIWrapper(params=params)
results = search.run("cafe")
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Ran <rccalman@gmail.com>
Because Milvus doesn't support nullable fields, but document metadata is
very rich, so it makes more sense to store it as json.
https://github.com/milvus-io/pymilvus/issues/1705#issuecomment-1731112372
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
BigQuery vector search lets you use GoogleSQL to do semantic search,
using vector indexes for fast but approximate results, or using brute
force for exact results.
This PR integrates LangChain vectorstore with BigQuery Vector Search.
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---------
Co-authored-by: Vlad Kolesnikov <vladkol@google.com>
- **Description:** replace score_threshold with args
- **Issue:** needs a way to pass more options to similarity search
- **Dependencies:** None
- **Twitter handle:** @workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
- **Description:** Tool now supports querying over 200 million
scientific articles, vastly expanding its reach beyond the 2 million
articles accessible through Arxiv. This update significantly broadens
access to the entire scope of scientific literature.
- **Dependencies:** semantischolar
https://github.com/danielnsilva/semanticscholar
- **Twitter handle:** @shauryr
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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…tch]: import models from community
ran
```bash
git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g"
git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g"
git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g"
git checkout master libs/langchain/tests/unit_tests/llms
git checkout master libs/langchain/tests/unit_tests/chat_models
git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py
make format
cd libs/langchain; make format
cd ../experimental; make format
cd ../core; make format
```
- easier to write custom logic/loops with automatic tracing
- if you don't want to streaming support write a regular function and
pass to RunnableLambda
- if you do want streaming write a generator and pass it to
RunnableGenerator
```py
import json
from typing import AsyncIterator
from langchain_core.messages import BaseMessage, FunctionMessage, HumanMessage
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import Runnable, RunnableGenerator, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.chat_models import ChatOpenAI
from langchain.tools.render import format_tool_to_openai_function
def _get_tavily():
from langchain.tools.tavily_search import TavilySearchResults
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
tavily_search = TavilySearchAPIWrapper()
return TavilySearchResults(api_wrapper=tavily_search)
async def _agent_executor_generator(
input: AsyncIterator[list[BaseMessage]],
*,
max_iterations: int = 10,
tools: dict[str, BaseTool],
agent: Runnable[list[BaseMessage], BaseMessage],
parser: Runnable[BaseMessage, AgentAction | AgentFinish],
) -> AsyncIterator[BaseMessage]:
messages = [m async for mm in input for m in mm]
for _ in range(max_iterations):
next_message = await agent.ainvoke(messages)
yield next_message
messages.append(next_message)
parsed = await parser.ainvoke(next_message)
if isinstance(parsed, AgentAction):
result = await tools[parsed.tool].ainvoke(parsed.tool_input)
next_message = FunctionMessage(name=parsed.tool, content=json.dumps(result))
yield next_message
messages.append(next_message)
elif isinstance(parsed, AgentFinish):
return
def get_agent_executor(tools: list[BaseTool], system_message: str):
llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, streaming=True)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_message),
MessagesPlaceholder(variable_name="messages"),
]
)
llm_with_tools = llm.bind(
functions=[format_tool_to_openai_function(t) for t in tools]
)
agent = {"messages": RunnablePassthrough()} | prompt | llm_with_tools
parser = OpenAIFunctionsAgentOutputParser()
executor = RunnableGenerator(_agent_executor_generator)
return executor.bind(
tools={tool.name for tool in tools}, agent=agent, parser=parser
)
agent = get_agent_executor([_get_tavily()], "You are a very nice agent!")
async def main():
async for message in agent.astream(
[HumanMessage(content="whats the weather in sf tomorrow?")]
):
print(message)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
results in this trace
https://smith.langchain.com/public/fa17f05d-9724-4d08-8fa1-750f8fcd051b/r
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- **Description:** SingleFileFacebookMessengerChatLoader did not handle
the case for when messages had stickers and/or photos so fixed that.
- **Issue:** #15356
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** updates/enhancements to IBM
[watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider
(prompt tuned models and prompt templates deployments support)
- **Dependencies:**
[ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
- **Tag maintainer:** : @hwchase17 , @eyurtsev , @baskaryan
- **Twitter handle:** details in comment below.
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally. ✅
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The fix#14221 has broken default gitlab url which is forcing the users
to specify GITLAB_URL for default one. With this fix if GITLAB_URL is
not set, the default gitlab url will be taken.
- **Description:** Add the GITHUB URL instead of None
- **Issue:** the issue #14221 has broken the default github URL
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
- **Twitter handle:** manjunath_shiva
- **Description:** This PR adds `api_base` to `_client_params` in the
`chat_model` of LiteLLM to ensure it's included in API calls.
Previously, `api_base` was set on the client but was not included in the
parameters passed to the completion function. This change ensures that
`api_base` is correctly passed to all API calls.
- **Issue:** #14338
- **Tag maintainer:** @hwchase17 @agola11
- **Twitter handle:** @LMS_David_RS
Sometimes, the tool_schema is like:
` {'action_name': 'search_items', 'action': {'term': 'pizza'}}`
sometimes, specially with gpt3.5 it comes like:
`{'action_name': 'search_items', 'term': 'pizza'}`
and it fails.
This PR is a way to make it work in both scenarios.
issues releated: #6624
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Co-authored-by: Lucca Zenobio <lucca.zenobio@ifood.com.br>
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This change addresses the issue where DashScopeEmbeddingAPI limits
requests to 25 lines of data, and DashScopeEmbeddings did not handle
cases with more than 25 lines, leading to errors. I have implemented a
fix to manage data exceeding this limit efficiently.
---------
Co-authored-by: xuxiang <xuxiang@aliyun.com>
Adding to my previously, already merged PR I made some further
improvements:
* Added documentation to the existing Pydantic Parser notebook, with an
example using LCEL and `with_retry()` on `OutputParserException`.
* Added an additional output example to the prompt
* More lenient parser in terms of LLM output format
* Amended unit test
FYI @hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Update _retrieve_ref inside json_schema.py to include
an isdigit() check
- **Issue:** This library is used inside dereference_refs inside
langchain_community.agent_toolkits.openapi.spec. When I read in a yaml
file which has references for "400", "401" etc; the line "out =
out[component]" causes a KeyError. The isdigit() check ensures that if
it is an integer like "400" or "401"; it converts it into integer before
using it as a key to prevent the error.
- **Dependencies:** No dependencies
- **Tag maintainer:** @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Description: _python-lint_
This agent writes Python code that is formatted and linted using
`black`, `ruff`, and `mypy`, but does not execute the code. It writes
the code to a temporary file and then runs the linters. Once these
checks pass, the code is returned.
# Dependencies
- black
- ruff
- mypy
# Demo
The functionality can be seen here:
https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo
Added some Headers in steam tool notebook to match consistency with the
other toolkit notebooks
- Dependencies: no new dependencies
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
`integrations/document_loaders/` `Excel` and `OneNote` pages in the
navbar were in the wrong sort order. It is because the file names are
not equal to the page titles.
- renamed `excel` and `onenote` file names
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@baskaryan, @eyurtsev, @hwchase17.
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- **Description:** Using PGVector vector store, it was only possible to
filter for values equals, in or not in metadata. Extended this feature
to work with the following keywords : IN, NIN, BETWEEN, GT, LT, NE, EQ,
LIKE, CONTAINS, OR, AND
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The regex used to match "Action" and "Action Input" in the output parser
has been updated. Previously, the regex did not correctly handle
multi-line inputs for "Action Input". The updated code uses the
're.DOTALL' flag to ensure multi-line inputs are correctly captured.
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@baskaryan, @eyurtsev, @hwchase17.
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:**
- This PR introduces a significant enhancement to the LangChain project
by integrating a new chat model powered by the third-generation base
large model, ChatGLM3, via the zhipuai API.
- This advanced model supports functionalities like function calls, code
interpretation, and intelligent Agent capabilities.
- The additions include the chat model itself, comprehensive
documentation in the form of Python notebook docs, and thorough testing
with both unit and integrated tests.
- **Dependencies:** This update relies on the ZhipuAI package as a key
dependency.
- **Twitter handle:** If this PR receives spotlight attention, we would
be honored to receive a mention for our integration of the advanced
ChatGLM3 model via the ZhipuAI API. Kindly tag us at @kaiwu.
To ensure quality and standards, we have performed extensive linting and
testing. Commands such as make format, make lint, and make test have
been run from the root of the modified package to ensure compliance with
LangChain's coding standards.
TO DO: Continue refining and enhancing both the unit tests and
integrated tests.
---------
Co-authored-by: jing <jingguo92@gmail.com>
Co-authored-by: hyy1987 <779003812@qq.com>
Co-authored-by: jianchuanqi <qijianchuan@hotmail.com>
Co-authored-by: lirq <whuclarence@gmail.com>
Co-authored-by: whucalrence <81530213+whucalrence@users.noreply.github.com>
Co-authored-by: Jing Guo <48378126+JaneCrystall@users.noreply.github.com>
Description: Volcano Ark is an enterprise-grade large-model service
platform for developers, providing a full range of functions and
services such as model training, inference, evaluation, fine-tuning. You
can visit its homepage at https://www.volcengine.com/docs/82379/1099455
for details. This change could help developers use the platform for
embedding.
Issue: None
Dependencies: volcengine
Tag maintainer: @baskaryan
Twitter handle: @hinnnnnnnnnnnns
---------
Co-authored-by: lujingxuansc <lujingxuansc@bytedance.com>
Updated prompt input suggestions
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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- **Description:** updated the outdated code in the document that was
generating the error,
- **Issue:** #15086 ,
- **Dependencies:** N/A,
- **Twitter handle:** [@vardhaman722](https://twitter.com/vardhaman722)
**Description:** the MWDumpLoader implementation currently does not
support the lazy_load method, and the files are usually very large. We
are proposing refactoring the load function, extracting two private
functions with the functionality of loading the dump file and parsing a
single page, to reuse the code in the lazy_load implementation.
**Description:**
This PR adds the `**kwargs` parameter to six calls in the `chroma.py`
package. All functions already were able to receive `kwargs` but they
were discarded before.
**Issue:**
When passing `kwargs` to functions in the `chroma.py` package they are
being ignored.
For example:
```
chroma_instance.similarity_search_with_score(
query,
k=100,
include=["metadatas", "documents", "distances", "embeddings"], # this parameter gets ignored
)
```
The `include` parameter does not get passed on to the next function and
does not have any effect.
**Dependencies:**
None
The quickstart doc is missing a few but very simple things that without
them, the code does not work. This PR fixes that by
- Adding commands to install `tiktoken` and `langchainhub`
- Adds a comma between 2 parameters for one of the methods
- **Description:** Fix a few spelling and grammar issues
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** @donovancmuller
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- **Description:** This PR corrects a documentation error in the
`ollama` usage tutorial. Specifically, it fixes a missing `])` in the
`CallbackManager()` example, ensuring that the code snippet is
syntactically correct and can be successfully executed.
- **Issue:** N/A
- **Dependencies:** No additional dependencies are required for this
change.
- **Twitter handle:** My twitter is @yhzhu99
Updated comment for better understanding
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- **Description:**
- support custom kwargs in object initialization. For instantance, QPS
differs from multiple object(chat/completion/embedding with diverse
models), for which global env is not a good choice for configuration.
- **Issue:** no
- **Dependencies:** no
- **Twitter handle:** no
@baskaryan PTAL
These can happen for edge cases not covered by `default` handler (eg.
"strange" keys in dicts)
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- Any direct usage of ThreadPoolExecutor or asyncio.run_in_executor
needs manual handling of context vars
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- **Description:** fix parse issue for AIMessageChunk when using
- **Issue:** https://github.com/langchain-ai/langchain/issues/14511
- **Dependencies:** none
- **Twitter handle:** none
Taken from this fix:
https://github.com/gpt-engineer-org/gpt-engineer/issues/804#issuecomment-1769853850
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
removed bad comments
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- **Description:** fixes and upgrades for the Tongyi LLM and ChatTongyi
Model
- Fixed typos; it should be `Tongyi`, not `OpenAI`.
- Fixed a bug in `stream_generate_with_retry`; it's a real stream
generator now.
- Fixed a bug in `validate_environment`; the `dashscope_api_key` should
be properly handled when set by environment variables or initialization
parameters.
- Changed the `dashscope` response to incremental output by setting the
parameter `incremental_output`, which eliminates the need for the
prefix-removal trick.
- Removed some unused parameters, like `n`, `prefix_messages`.
- Added `_stream` method.
- Added async methods support, such as `_astream`, `_agenerate`,
`_abatch`.
- **Dependencies:** No new dependencies.
- **Tag maintainer:** @hwchase17
> PS: Some may be confused about the terms `dashscope`, `tongyi`, and
`Qwen`:
> - `dashscope`: A platform to deploy LLMs and provide APIs to invoke
the LLM.
> - `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI.
> - `Qwen`: An LLM that is open-sourced and deployed in `dashscope`.
>
> We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Correcting a small typo ('the' instead of 'then') and changing another
'the' (instead of 'then' too, it was a hard day for the 'n' key :D) to
'also' to match better with what is done in the code
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- **Description:** in the code_understanding.ipynb example, the loader
errors out on the
langchain/libs/community/tests/examples/non-utf8-encoding.py file, so I
updated the loader to exclude that file. Excluding that file allows the
example to run.
- **Issue:** not applicable
- **Dependencies:** none
- do not match text after - in the middle of a sentence
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…parse
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```shell
Python 3.11.6 (main, Nov 2 2023, 04:39:43) [Clang 14.0.3 (clang-1403.0.22.14.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> s = {'name': 'gc', 'arguments': '{"prompt":"hi\nbob."}'}
>>> import json
>>> json.loads(s['arguments'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
^^^^^^^^^^^^^^^^^^^^^^
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 14 (char 13)
>>> json.loads(s['arguments'].replace('\n', '\\n'))
{'prompt': 'hi\nbob.'}
>>>
```
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
While using `chain.batch`, the default implementation uses a
`ThreadPoolExecutor` and run the chains in separate threads. An issue
with this approach is that that [the token counting
callback](https://python.langchain.com/docs/modules/callbacks/token_counting)
fails to work as a consequence of the context not being propagated
between threads. This PR adds context propagation to the new threads and
adds some thread synchronization in the OpenAI callback. With this
change, the token counting callback works as intended.
Having the context propagation change would be highly beneficial for
those implementing custom callbacks for similar functionalities as well.
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
- Enables strict=False by default
- Uses partial json recovery logic by default
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- **Description:** The Github error prompt is confused because of JWT
enctrypt to somebody not familiar with Github connection method. This PR
is to add some useful error prompt to help users troubleshooting.
- **Issue:**
https://github.com/langchain-ai/langchain/issues/14550#issuecomment-1867445049
- **Dependencies:** None,
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…ableBinding
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…unnableAssign or RunnablePick
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…ching documentation
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- **Description:** Fixed the wrong output and code block comment in
`Upstash Redis` Cache section of LLM Caching documentation,
- **Issue:** #15139 ,
- **Dependencies:** N/A,
- **Twitter handle:** [@vardhaman722](https://twitter.com/vardhaman722)
**Description:**
Adding async methods to booth OllamaLLM and ChatOllama to enable async
streaming and async .on_llm_new_token callbacks.
**Issue:**
ChatOllama is not working in combination with an AsyncCallbackManager
because the .on_llm_new_token method is not awaited.
**Description:** `decouple` is not the correct package, it's
`python-decouple`, and the notebook cell doesn't compile.
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This document uses Oxford comma (A, B, and C), in this list the comma
was missing before "and".
This PR corrects that.
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- Added ensure_ascii property to ElasticsearchChatMessageHistory
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---------
Co-authored-by: Ivan Chetverikov <ivan.chetverikov@raftds.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description**: The parameter chunk_type was being hard coded to
"extractive_answers", so that when "snippet" was being passed, it was
being ignored. This change simply doesn't do that.
Added the call function get_summaries_as_docs inside of Arxivloader
- **Description:** Added a function that returns the documents from
get_summaries_as_docs, as the call signature is present in the parent
file but never used from Arxivloader, this can be used from Arxivloader
itself just like .load() as both the signatures are same.
- **Issue:** Reduces time to load papers as no pdf is processed only
metadata is pulled from Arxiv allowing users for faster load times on
bulk loads. Users can then choose one or more paper and use ID directly
with .load() to load pdf thereby loading all the contents of the paper.
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## Description
Changes the behavior of `add_user_message` and `add_ai_message` to allow
for messages of those types to be passed in. Currently, if you want to
use the `add_user_message` or `add_ai_message` methods, you have to pass
in a string. For `add_message` on `ChatMessageHistory`, however, you
have to pass a `BaseMessage`. This behavior seems a bit inconsistent.
Personally, I'd love to be able to be explicit that I want to
`add_user_message` and pass in a `HumanMessage` without having to grab
the `content` attribute. This PR allows `add_user_message` to accept
`HumanMessage`s or `str`s and `add_ai_message` to accept `AIMessage`s or
`str`s to add that functionality and ensure backwards compatibility.
## Issue
* None
## Dependencies
* None
## Tag maintainer
@hinthornw
@baskaryan
## Note
`make test` results in `make: *** No rule to make target 'test'. Stop.`
- **Description:** `tools.gmail.send_message` implements a
`SendMessageSchema` that is not used anywhere. `GmailSendMessage` also
does not have an `args_schema` attribute (this led to issues when
invoking the tool with an OpenAI functions agent, at least for me). Here
we add the missing attribute and a minimal test for the tool.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
---------
Co-authored-by: Chester Curme <chestercurme@microsoft.com>
Fixing typos: it's -> its
Fixing grammatical mistakes:
* having to worry -> worrying
* convert -> converts
* few main types -> a few main types
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
add_video_info should be false in the first example
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- **Description:** In response to user feedback, this PR refactors the
Baseten integration with updated model endpoints, as well as updates
relevant documentation. This PR has been tested by end users in
production and works as expected.
- **Issue:** N/A
- **Dependencies:** This PR actually removes the dependency on the
`baseten` package!
- **Twitter handle:** https://twitter.com/basetenco
# Description
This PR adds the ability to pass a `botocore.config.Config` instance to
the boto3 client instantiated by the Bedrock LLM.
Currently, the Bedrock LLM doesn't support a way to pass a Config, which
means that some settings (e.g., timeouts and retry configuration)
require instantiating a new boto3 client with a Config and then
replacing the LLM's client:
```python
llm = Bedrock(
region_name='us-west-2',
model_id="anthropic.claude-v2",
model_kwargs={'max_tokens_to_sample': 4096, 'temperature': 0},
)
llm.client = boto_client('bedrock-runtime', region_name='us-west-2', config=Config({'read_timeout': 300}))
```
# Issue
N/A
# Dependencies
N/A
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fix spellings
**seperate -> separate**: found more occurrences, see
https://github.com/langchain-ai/langchain/pull/14602
**initialise -> intialize**: the latter is more common in the repo
**pre-defined > predefined**: adding a comma after a prefix is a
delicate matter, but this is a generally accepted word
also, another word that appears in the repo is "fs" (stands for
filesystem), e.g., in `libs/core/langchain_core/prompts/loading.py`
` """Unified method for loading a prompt from LangChainHub or local
fs."""`
Isn't "filesystem" better?
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Ran <rccalman@gmail.com>
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- **Description:** This PR fixes test failures on Windows caused by path
handling differences and unescaped special characters in regex. The
failing tests are:
```
FAILED tests/unit_tests/storage/test_filesystem.py::test_yield_keys - AssertionError: assert ['key1', 'subdir\\key2'] == ['key1', 'subdir/key2']
FAILED tests/unit_tests/test_imports.py::test_importable_all - ModuleNotFoundError: No module named 'langchain_community.langchain_community\\adapters'
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_absolute - re.error: incomplete escape \U at position 53
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_parent_dir - re.error: incomplete escape \U at position 69
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_for_symlink_outside_root - re.error: incomplete escape \U at position 64
```
- **Issue:** fixes
https://github.com/langchain-ai/langchain/issues/11775 (partially)
- **Dependencies:** none
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Replace this entire comment with:
- **Description:** @kurtisvg has raised a point that it's a good idea to
have a fixed version for embeddings (since otherwise a user might run a
query with one version vs a vectorstore where another version was used).
In order to avoid breaking changes, I'd suggest to give users a warning,
and make a `model_name` a required argument in 1.5 months.
Surrealdb client changes from 0.3.1 to 0.3.2 broke the surrealdb vectore
integration.
This PR updates the code to work with the updated client. The change is
backwards compatible with previous versions of surrealdb client.
Also expanded the vector store implementation to store and retrieve
metadata that's included with the document object.
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- **Description:** Fixed jaguar.py to import JaguarHttpClient with try
and catch
- **Issue:** the issue # Unable to use the JaguarHttpClient at run time
- **Dependencies:** It requires "pip install -U jaguardb-http-client"
- **Twitter handle:** workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
For the Momento Vector Index (MVI) vector store implementation, pass
through `filter_expression` kwarg to the MVI client, if specified. This
change will enable the MVI self query implementation in a future PR.
Also fixes some integration tests.
- **Description:** Fix typo in class Docstring to replace
AZURE_OPENAI_API_ENDPOINT by AZURE_OPENAI_ENDPOINT
- **Issue:** the issue #14901
- **Dependencies:** NA
- **Twitter handle:**
Co-authored-by: Yacine Bouakkaz <Yacine.Bouakkaz@evokegroup.com>
* This PR adds `stream` implementations to Runnable Branch.
* Runnable Branch still does not support `transform` so it'll break streaming if it happens in middle or end of sequence, but will work if happens at beginning of sequence.
* Fixes use the async callback manager for async methods
* Handle BaseException rather than Exception, so more errors could be logged as errors when they are encountered
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Description: Adding Summarization to Vectara, to reflect it provides not
only vector-store type functionality but also can return a summary.
Also added:
MMR capability (in the Vectara platform side)
Updated templates
Updated documentation and IPYNB examples
Tag maintainer: @baskaryan
Twitter handle: @ofermend
---------
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
**What is the reproduce code?**
```python
from langchain.chains import LLMChain, load_chain
from langchain.llms import Databricks
from langchain.prompts import PromptTemplate
def transform_output(response):
# Extract the answer from the responses.
return str(response["candidates"][0]["text"])
def transform_input(**request):
full_prompt = f"""{request["prompt"]}
Be Concise.
"""
request["prompt"] = full_prompt
return request
chat_model = Databricks(
endpoint_name="llama2-13B-chat-Brambles",
transform_input_fn=transform_input,
transform_output_fn=transform_output,
verbose=True,
)
print(f"Test chat model: {chat_model('What is Apache Spark')}") # This works
llm_chain = LLMChain(llm=chat_model, prompt=PromptTemplate.from_template("{chat_input}"))
llm_chain("colorful socks") # this works
llm_chain.save("databricks_llm_chain.yaml") # transform_input_fn and transform_output_fn are not serialized into the model yaml file
loaded_chain = load_chain("databricks_llm_chain.yaml") # The Databricks LLM is recreated with transform_input_fn=None, transform_output_fn=None.
loaded_chain("colorful socks") # Thus this errors. The transform_output_fn is needed to produce the correct output
```
Error:
```
File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-6c34afab-3473-421d-877f-1ef18930ef4d/lib/python3.10/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for Generation
text
str type expected (type=type_error.str)
request payload: {'query': 'What is a databricks notebook?'}'}
```
**What does the error mean?**
When the LLM generates an answer, represented by a Generation data
object. The Generation data object takes a str field called text, e.g.
Generation(text=”blah”). However, the Databricks LLM tried to put a
non-str to text, e.g. Generation(text={“candidates”:[{“text”: “blah”}]})
Thus, pydantic errors.
**Why the output format becomes incorrect after saving and loading the
Databricks LLM?**
Databrick LLM does not support serializing transform_input_fn and
transform_output_fn, so they are not serialized into the model yaml
file. When the Databricks LLM is loaded, it is recreated with
transform_input_fn=None, transform_output_fn=None. Without
transform_output_fn, the output text is not unwrapped, thus errors.
Missing transform_output_fn causes this error.
Missing transform_input_fn causes the additional prompt “Be Concise.” to
be lost after saving and loading.
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-12-20 12:50:23 -05:00
2177 changed files with 131077 additions and 78612 deletions
description:Please confirm and check all the following options.
options:
- label:I added a very descriptive title to this issue.
required:true
- label:I searched the LangChain documentation with the integrated search.
required:true
- label:I used the GitHub search to find a similar question and didn't find it.
required:true
- type:textarea
id:reproduction
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required:true
attributes:
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description:|
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
If you're including an error message, please include the full stack trace not just the last error.
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placeholder:|
The following code:
```python
from langchain_core.runnables import RunnableLambda
def bad_code(inputs) -> int:
raise NotImplementedError('For demo purpose')
chain = RunnableLambda(bad_code)
chain.invoke('Hello!')
```
Include both the error and the full stack trace if reporting an exception!
- type:textarea
id:description
attributes:
label:Description
description:|
What is the problem, question, or error?
Write a short description telling what you are doing, what you expect to happen, and what is currently happening.
placeholder:|
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* I expect to see Y.
* Instead, it does Z.
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description:Please share your system info with us.
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If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
The core maintainers strive to read all issues, but tagging them will help them prioritize.
Please tag fewer than 3 people.
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoader Abstractions
- @eyurtsev
LLM/Chat Wrappers
- @hwchase17
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Tools / Toolkits
- ...
placeholder:"@Username ..."
- type:checkboxes
id:information-scripts-examples
attributes:
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description:"The problem arises when using:"
options:
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- label:"My own modified scripts"
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attributes:
@@ -77,30 +101,3 @@ body:
- label:"Chains"
- label:"Callbacks/Tracing"
- label:"Async"
- type:textarea
id:reproduction
validations:
required:true
attributes:
label:Reproduction
description:|
Please provide a [code sample](https://stackoverflow.com/help/minimal-reproducible-example) that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
If you have code snippets, error messages, stack traces please provide them here as well.
Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
placeholder:|
Steps to reproduce the behavior:
1.
2.
3.
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description:"A clear and concise description of what you would expect to happen."
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
@@ -95,7 +95,7 @@ Agents involve an LLM making decisions about which Actions to take, taking that
Please see [here](https://python.langchain.com) for full documentation, which includes:
- [Getting started](https://python.langchain.com/docs/get_started/introduction): installation, setting up the environment, simple examples
- Overview of the [interfaces](https://python.langchain.com/docs/expression_language/), [modules](https://python.langchain.com/docs/modules/) and [integrations](https://python.langchain.com/docs/integrations/providers)
- Overview of the [interfaces](https://python.langchain.com/docs/expression_language/), [modules](https://python.langchain.com/docs/modules/), and [integrations](https://python.langchain.com/docs/integrations/providers)
- [Use case](https://python.langchain.com/docs/use_cases/qa_structured/sql) walkthroughs and best practice [guides](https://python.langchain.com/docs/guides/adapters/openai)
- [LangSmith](https://python.langchain.com/docs/langsmith/), [LangServe](https://python.langchain.com/docs/langserve), and [LangChain Template](https://python.langchain.com/docs/templates/) overviews
- [Reference](https://api.python.langchain.com): full API docs
"template = \"\"\"Given an input question, convert it to a SQL query. No pre-amble. Based on the table schema below, write a SQL query that would answer the user's question:\n",
"A seperate cookbook highlights `Option 1` [here](https://github.com/langchain-ai/langchain/blob/master/cookbook/multi_modal_RAG_chroma.ipynb).\n",
"A separate cookbook highlights `Option 1` [here](https://github.com/langchain-ai/langchain/blob/master/cookbook/multi_modal_RAG_chroma.ipynb).\n",
"\n",
"And option `Option 2` is appropriate for cases when a multi-modal LLM cannot be used for answer synthesis (e.g., cost, etc).\n",
"\n",
@@ -101,7 +101,7 @@
"If you want to use the provided folder, then simply opt for a [pdf loader](https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf) for the document:\n",
"Add raw docs and doc summaries to [Multi Vector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary): \n",
"\n",
"* Store the raw texts, tables, and images in the `docstore`.\n",
"* Store the texts, table summaries, and image summaries in the `vectorstore` for semantic retrieval."
"* Store the texts, table summaries, and image summaries in the `vectorstore` for efficient semantic retrieval."
" \"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\"\n",
" 1. Upload all python project files using the `langchain.document_loaders.TextLoader`. We will call these files the **documents**.\n",
" 1. Upload all python project files using the `langchain_community.document_loaders.TextLoader`. We will call these files the **documents**.\n",
" 2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.\n",
" 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain.vectorstores.DeepLake`\n",
" 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain_community.vectorstores.DeepLake`\n",
"2. Question-Answering:\n",
" 1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
"This notebook builds off of [this notebook](/docs/modules/agents/how_to/custom_llm_agent) and assumes familiarity with how agents work.\n",
"\n",
"The novel idea introduced in this notebook is the idea of using retrieval to select the set of tools to use to answer an agent query. This is useful when you have many many tools to select from. You cannot put the description of all the tools in the prompt (because of context length issues) so instead you dynamically select the N tools you do want to consider using at run time.\n",
"\n",
"In this notebook we will create a somewhat contrived example. We will have one legitimate tool (search) and then 99 fake tools which are just nonsense. We will then add a step in the prompt template that takes the user input and retrieves tool relevant to the query."
"- To use Azure embeddings with OpenAI V1, you'll need to use the new `AzureOpenAIEmbeddings` instead of the existing `OpenAIEmbeddings`. `OpenAIEmbeddings` continue to work when using Azure with `openai<1`.\n",
"template = \"\"\"You are a Postgres expert. Given an input question, first create a syntactically correct Postgres query to run, then look at the results of the query and return the answer to the input question.\n",
"Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per Postgres. You can order the results to return the most informative data in the database.\n",
"'\\nAnswer: The architectural details of Mixtral are as follows:\\n- Dimension (dim): 4096\\n- Number of layers (n\\\\_layers): 32\\n- Dimension of each head (head\\\\_dim): 128\\n- Hidden dimension (hidden\\\\_dim): 14336\\n- Number of heads (n\\\\_heads): 32\\n- Number of kv heads (n\\\\_kv\\\\_heads): 8\\n- Context length (context\\\\_len): 32768\\n- Vocabulary size (vocab\\\\_size): 32000\\n- Number of experts (num\\\\_experts): 8\\n- Number of top k experts (top\\\\_k\\\\_experts): 2\\n\\nMixtral is based on a transformer architecture and uses the same modifications as described in [18], with the notable exceptions that Mixtral supports a fully dense context length of 32k tokens, and the feedforward block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router network chooses two of these groups (the “experts”) to process the token and combine their output additively. This technique increases the number of parameters of a model while controlling cost and latency, as the model only uses a fraction of the total set of parameters per token. Mixtral is pretrained with multilingual data using a context size of 32k tokens. It either matches or exceeds the performance of Llama 2 70B and GPT-3.5, over several benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks.'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"What are the Architectural details of Mixtral?\")"
Hi! Thanks for being here. We’re lucky to have a community of so many passionate developers building with LangChain–we have so much to teach and learn from each other. Community members contribute code, host meetups, write blog posts, amplify each other’s work, become each other's customers and collaborators, and so much more.
Whether you’re new to LangChain, looking to go deeper, or just want to get more exposure to the world of building with LLMs, this page can point you in the right direction.
- **🦜 Contribute to LangChain**
- **🌍Meetups, Events, and Hackathons**
- **📣 Help Us Amplify Your Work**
- **💬 Stay in the loop**
# 🦜 Contribute to LangChain
LangChain is the product of over 5,000+ contributions by 1,500+ contributors, and there is ******still****** so much to do together. Here are some ways to get involved:
- **[Open a pull request](https://github.com/langchain-ai/langchain/issues):** We’d appreciate all forms of contributions–new features, infrastructure improvements, better documentation, bug fixes, etc. If you have an improvement or an idea, we’d love to work on it with you.
- **[Read our contributor guidelines:](./contributing/)** We ask contributors to follow a["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects)workflow, run a few local checks for formatting, linting, and testing before submitting, and follow certain documentation and testing conventions.
- **First time contributor?** [Try one of these PRs with the “good first issue” tag](https://github.com/langchain-ai/langchain/contribute).
- **Become an expert:** Our experts help the community by answering product questions in Discord. If that’s a role you’d like to play, we’d be so grateful! (And we have some special experts-only goodies/perks we can tell you more about). Send us an email to introduce yourself at hello@langchain.dev and we’ll take it from there!
- **Integrate with LangChain:** If your product integrates with LangChain–or aspires to–we want to help make sure the experience is as smooth as possible for you and end users. Send us an email at hello@langchain.dev and tell us what you’re working on.
- **Become an Integration Maintainer:** Partner with our team to ensure your integration stays up-to-date and talk directly with users (and answer their inquiries) in our Discord. Introduce yourself at hello@langchain.dev if you’d like to explore this role.
# 🌍 Meetups, Events, and Hackathons
One of our favorite things about working in AI is how much enthusiasm there is for building together. We want to help make that as easy and impactful for you as possible!
- **Find a meetup, hackathon, or webinar:** You can find the one for you on our [global events calendar](https://mirror-feeling-d80.notion.site/0bc81da76a184297b86ca8fc782ee9a3?v=0d80342540df465396546976a50cfb3f).
- **Submit an event to our calendar:** Email us at events@langchain.dev with a link to your event page! We can also help you spread the word with our local communities.
- **Host a meetup:** If you want to bring a group of builders together, we want to help! We can publicize your event on our event calendar/Twitter, share it with our local communities in Discord, send swag, or potentially hook you up with a sponsor. Email us at events@langchain.dev to tell us about your event!
- **Become a meetup sponsor:** We often hear from groups of builders that want to get together, but are blocked or limited on some dimension (space to host, budget for snacks, prizes to distribute, etc.). If you’d like to help, send us an email to events@langchain.dev we can share more about how it works!
- **Speak at an event:** Meetup hosts are always looking for great speakers, presenters, and panelists. If you’d like to do that at an event, send us an email to hello@langchain.dev with more information about yourself, what you want to talk about, and what city you’re based in and we’ll try to match you with an upcoming event!
- **Tell us about your LLM community:** If you host or participate in a community that would welcome support from LangChain and/or our team, send us an email at hello@langchain.dev and let us know how we can help.
# 📣Help Us Amplify Your Work
If you’re working on something you’re proud of, and think the LangChain community would benefit from knowing about it, we want to help you show it off.
- **Post about your work and mention us:** We love hanging out on Twitter to see what people in the space are talking about and working on. If you tag [@langchainai](https://twitter.com/LangChainAI), we’ll almost certainly see it and can show you some love.
- **Publish something on our blog:** If you’re writing about your experience building with LangChain, we’d love to post (or crosspost) it on our blog! E-mail hello@langchain.dev with a draft of your post! Or even an idea for something you want to write about.
- **Get your product onto our [integrations hub](https://integrations.langchain.com/):** Many developers take advantage of our seamless integrations with other products, and come to our integrations hub to find out who those are. If you want to get your product up there, tell us about it (and how it works with LangChain) at hello@langchain.dev.
# ☀️ Stay in the loop
Here’s where our team hangs out, talks shop, spotlights cool work, and shares what we’re up to. We’d love to see you there too.
- **[Twitter](https://twitter.com/LangChainAI):** We post about what we’re working on and what cool things we’re seeing in the space. If you tag @langchainai in your post, we’ll almost certainly see it, and can show you some love!
- **[Discord](https://discord.gg/6adMQxSpJS):** connect with over 30,000 developers who are building with LangChain.
- **[GitHub](https://github.com/langchain-ai/langchain):** Open pull requests, contribute to a discussion, and/or contribute
- **[Subscribe to our bi-weekly Release Notes](https://6w1pwbss0py.typeform.com/to/KjZB1auB):** a twice/month email roundup of the coolest things going on in our orbit
Partner packages are in `libs/partners/*` and are installed by users with `pip install langchain-{partner}`, and exported members can be imported with code like
As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
a maintainer and published to [PyPI](https://pypi.org/).
The different packages are versioned slightly differently.
## `langchain-core`
`langchain-core` is currently on version `0.1.x`.
As `langchain-core` contains the base abstractions and runtime for the whole LangChain ecosystem, we will communicate any breaking changes with advance notice and version bumps. The exception for this is anything in `langchain_core.beta`. The reason for `langchain_core.beta` is that given the rate of change of the field, being able to move quickly is still a priority, and this module is our attempt to do so.
Minor version increases will occur for:
- Breaking changes for any public interfaces NOT in `langchain_core.beta`
Patch version increases will occur for:
- Bug fixes
- New features
- Any changes to private interfaces
- Any changes to `langchain_core.beta`
## `langchain`
`langchain` is currently on version `0.0.x`
All changes will be accompanied by a patch version increase. Any changes to public interfaces are nearly always done in a backwards compatible way and will be communicated ahead of time when they are not backwards compatible.
We are targeting January 2024 for a release of `langchain` v0.1, at which point `langchain` will adopt the same versioning policy as `langchain-core`.
## `langchain-community`
`langchain-community` is currently on version `0.0.x`
All changes will be accompanied by a patch version increase.
## `langchain-experimental`
`langchain-experimental` is currently on version `0.0.x`
All changes will be accompanied by a patch version increase.
## Partner Packages
Partner packages are versioned independently.
# 🌟 Recognition
If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m <tool>search</tool>\n",
"<tool_input>weather in new york\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"\n",
"<final_answer>The weather in New York is 32 degrees\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m <tool>search</tool><tool_input>weather in New York\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m <tool>search</tool>\n",
"<tool_input>weather in New York\u001b[0m\u001b[36;1m\u001b[1;3m32 degrees\u001b[0m\u001b[32;1m\u001b[1;3m <final_answer>The weather in New York is 32 degrees\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@@ -159,17 +159,17 @@
{
"data": {
"text/plain": [
"{'question': 'whats the weather in New york?',\n",
"{'input': 'whats the weather in New york?',\n",
" 'output': 'The weather in New York is 32 degrees'}"
]
},
"execution_count": 9,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.invoke({\"question\": \"whats the weather in New york?\"})"
"agent_executor.invoke({\"input\": \"whats the weather in New york?\"})"
"prompt1 = ChatPromptTemplate.from_template(\"what is the city {person} is from?\")\n",
"prompt2 = ChatPromptTemplate.from_template(\n",
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