Before, we need to use `params` to pass extra parameters:
```python
from langchain.llms import Databricks
Databricks(..., params={"temperature": 0.0})
```
Now, we can directly specify extra params:
```python
from langchain.llms import Databricks
Databricks(..., temperature=0.0)
```
This PR adds an "Azure AI data" document loader, which allows Azure AI
users to load their registered data assets as a document object in
langchain.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
See PR title.
From what I can see, `poetry` will auto-include this. Please let me know
if I am missing something here.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
… properly
Fixed a bug that was causing the streaming transfer to not work
properly.
- **Description:
1、The on_llm_new_token method in the streaming callback can now be
called properly in streaming transfer mode.
2、In streaming transfer mode, LLM can now correctly output the complete
response instead of just the first token.
- **Tag maintainer: @wangxuqi
- **Twitter handle: @kGX7XJjuYxzX9Km
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
* Add support for passing a specific file to the file system blob loader
* Allow specifying a class parameter for the parser for the generic
loader
```python
class AudioLoader(GenericLoader):
@staticmethod
def get_parser(**kwargs):
return MyAudioParser(**kwargs):
```
The intent of the GenericLoader is to provide on-ramps from different
sources (e.g., web, s3, file system).
An alternative is to use pipelining syntax or creating a Pipeline
```
FileSystemBlobLoader(...) | MyAudioParser
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Change instances of RunnableMap to RunnableParallel,
as that should be the one used going forward. This makes it consistent
across the codebase.
### Description:
Doc addition for LCEL introduction. Adds a more basic starter guide for
using LCEL.
---------
Co-authored-by: Alex Kira <akira@Alexs-MBP.local.tld>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** just a little change of ErnieChatBot class
description, sugguesting user to use more suitable class
- **Issue:** none,
- **Dependencies:** none,
- **Tag maintainer:** @baskaryan ,
- **Twitter handle:** none
**Description**
`embed_with_retry` is for sync operations and not for async operations.
Use `async_embed_with_retry` for appropriate async operations.
I'm using `OpenAIEmbedding(http_client=httpx.AsyncClient())` with only
async operations.
However, I got an error when I use `embedding.aembed_documents` because
`embed_with_retry` uses sync OpenAI client with async http client.
Description
when the desc of arg in python docstring contains ":", the
`_parse_python_function_docstring` will raise **ValueError: too many
values to unpack (expected 2)**.
A sample desc would be:
"""
Args:
error_arg: this is an arg with an additional ":" symbol
"""
So, set `maxsplit` parameter to fix it.
The number of times I try to format a string (especially in lcel) is
embarrassingly high. Think this may be more actionable than the default
error message. Now I get nice helpful errors
```
KeyError: "Input to ChatPromptTemplate is missing variable 'input'. Expected: ['input'] Received: ['dialogue']"
```
### Description
Now if `example` in Message is False, it will not be displayed. Update
the output in this document.
```python
In [22]: m = HumanMessage(content="Text")
In [23]: m
Out[23]: HumanMessage(content='Text')
In [24]: m = HumanMessage(content="Text", example=True)
In [25]: m
Out[25]: HumanMessage(content='Text', example=True)
```
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617)
**Description:** By combining the document timestamp refresh within a
single call to update(), this enables batching of multiple documents in
a single SQL statement. This is important for non-local databases where
tens of milliseconds has a huge impact on performance when doing
document-by-document SQL statements.
**Issue:** #11935
**Dependencies:** None
**Tag maintainer:** @eyurtsev
- **Description:** Touch up of the documentation page for Metaphor
Search Tool integration. Removes documentation for old built-in tool
wrapper.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
CC @baskaryan @hwchase17 @jmorganca
Having a bit of trouble importing `langchain_experimental` from a
notebook, will figure it out tomorrow
~Ah and also is blocked by #13226~
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
Related to https://github.com/mlflow/mlflow/pull/10420. MLflow AI
gateway will be deprecated and replaced by the `mlflow.deployments`
module. Happy to split this PR if it's too large.
```
pip install git+https://github.com/langchain-ai/langchain.git@refs/pull/13699/merge#subdirectory=libs/langchain
```
## Dependencies
Install mlflow from https://github.com/mlflow/mlflow/pull/10420:
```
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/10420/merge
```
## Testing plan
The following code works fine on local and databricks:
<details><summary>Click</summary>
<p>
```python
"""
Setup
-----
mlflow deployments start-server --config-path examples/gateway/openai/config.yaml
databricks secrets create-scope <scope>
databricks secrets put-secret <scope> openai-api-key --string-value $OPENAI_API_KEY
Run
---
python /path/to/this/file.py secrets/<scope>/openai-api-key
"""
from langchain.chat_models import ChatMlflow, ChatDatabricks
from langchain.embeddings import MlflowEmbeddings, DatabricksEmbeddings
from langchain.llms import Databricks, Mlflow
from langchain.schema.messages import HumanMessage
from langchain.chains.loading import load_chain
from mlflow.deployments import get_deploy_client
import uuid
import sys
import tempfile
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
###############################
# MLflow
###############################
chat = ChatMlflow(
target_uri="http://127.0.0.1:5000", endpoint="chat", params={"temperature": 0.1}
)
print(chat([HumanMessage(content="hello")]))
embeddings = MlflowEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings")
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
llm = Mlflow(
target_uri="http://127.0.0.1:5000",
endpoint="completions",
params={"temperature": 0.1},
)
print(llm("I am"))
llm_chain = LLMChain(
llm=llm,
prompt=PromptTemplate(
input_variables=["adjective"],
template="Tell me a {adjective} joke",
),
)
print(llm_chain.run(adjective="funny"))
# serialization/deserialization
with tempfile.TemporaryDirectory() as tmpdir:
print(tmpdir)
path = f"{tmpdir}/llm.yaml"
llm_chain.save(path)
loaded_chain = load_chain(path)
print(loaded_chain("funny"))
###############################
# Databricks
###############################
secret = sys.argv[1]
client = get_deploy_client("databricks")
# External - chat
name = f"chat-{uuid.uuid4()}"
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "test",
"external_model": {
"name": "gpt-4",
"provider": "openai",
"task": "llm/v1/chat",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
try:
chat = ChatDatabricks(
target_uri="databricks", endpoint=name, params={"temperature": 0.1}
)
print(chat([HumanMessage(content="hello")]))
finally:
client.delete_endpoint(endpoint=name)
# External - embeddings
name = f"embeddings-{uuid.uuid4()}"
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "test",
"external_model": {
"name": "text-embedding-ada-002",
"provider": "openai",
"task": "llm/v1/embeddings",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
try:
embeddings = DatabricksEmbeddings(target_uri="databricks", endpoint=name)
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
finally:
client.delete_endpoint(endpoint=name)
# External - completions
name = f"completions-{uuid.uuid4()}"
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "test",
"external_model": {
"name": "gpt-3.5-turbo-instruct",
"provider": "openai",
"task": "llm/v1/completions",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
try:
llm = Databricks(
endpoint_name=name,
model_kwargs={"temperature": 0.1},
)
print(llm("I am"))
finally:
client.delete_endpoint(endpoint=name)
# Foundation model - chat
chat = ChatDatabricks(
endpoint="databricks-llama-2-70b-chat", params={"temperature": 0.1}
)
print(chat([HumanMessage(content="hello")]))
# Foundation model - embeddings
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
print(embeddings.embed_query("hello")[:3])
# Foundation model - completions
llm = Databricks(
endpoint_name="databricks-mpt-7b-instruct", model_kwargs={"temperature": 0.1}
)
print(llm("hello"))
llm_chain = LLMChain(
llm=llm,
prompt=PromptTemplate(
input_variables=["adjective"],
template="Tell me a {adjective} joke",
),
)
print(llm_chain.run(adjective="funny"))
# serialization/deserialization
with tempfile.TemporaryDirectory() as tmpdir:
print(tmpdir)
path = f"{tmpdir}/llm.yaml"
llm_chain.save(path)
loaded_chain = load_chain(path)
print(loaded_chain("funny"))
```
Output:
```
content='Hello! How can I assist you today?'
[-0.025058426, -0.01938856, -0.027781019]
[-0.025058426, -0.01938856, -0.027781019]
sorry, but I cannot continue the sentence as it is incomplete. Can you please provide more information or context?
Sure, here's a classic one for you:
Why don't scientists trust atoms?
Because they make up everything!
/var/folders/dz/cd_nvlf14g9g__n3ph0d_0pm0000gp/T/tmpx_4no6ad
{'adjective': 'funny', 'text': "Sure, here's a classic one for you:\n\nWhy don't scientists trust atoms?\n\nBecause they make up everything!"}
content='Hello! How can I assist you today?'
[-0.025058426, -0.01938856, -0.027781019]
[-0.025058426, -0.01938856, -0.027781019]
a 23 year old female and I am currently studying for my master's degree
content="\nHello! It's nice to meet you. Is there something I can help you with or would you like to chat for a bit?"
[0.051055908203125, 0.007221221923828125, 0.003879547119140625]
[0.051055908203125, 0.007221221923828125, 0.003879547119140625]
hello back
Well, I don't really know many jokes, but I do know this funny story...
/var/folders/dz/cd_nvlf14g9g__n3ph0d_0pm0000gp/T/tmp7_ds72ex
{'adjective': 'funny', 'text': " Well, I don't really know many jokes, but I do know this funny story..."}
```
</p>
</details>
The existing workflow doesn't break:
<details><summary>click</summary>
<p>
```python
import uuid
import mlflow
from mlflow.models import ModelSignature
from mlflow.types.schema import ColSpec, Schema
class MyModel(mlflow.pyfunc.PythonModel):
def predict(self, context, model_input):
return str(uuid.uuid4())
with mlflow.start_run():
mlflow.pyfunc.log_model(
"model",
python_model=MyModel(),
pip_requirements=["mlflow==2.8.1", "cloudpickle<3"],
signature=ModelSignature(
inputs=Schema(
[
ColSpec("string", "prompt"),
ColSpec("string", "stop"),
]
),
outputs=Schema(
[
ColSpec(name=None, type="string"),
]
),
),
registered_model_name=f"lang-{uuid.uuid4()}",
)
# Manually create a serving endpoint with the registered model and run
from langchain.llms import Databricks
llm = Databricks(endpoint_name="<name>")
llm("hello") # 9d0b2491-3d13-487c-bc02-1287f06ecae7
```
</p>
</details>
## Follow-up tasks
(This PR is too large. I'll file a separate one for follow-up tasks.)
- Update `docs/docs/integrations/providers/mlflow_ai_gateway.mdx` and
`docs/docs/integrations/providers/databricks.md`.
---------
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
…parameters.
In Langchain's `dumps()` function, I've added a `**kwargs` parameter.
This allows users to pass additional parameters to the underlying
`json.dumps()` function, providing greater flexibility and control over
JSON serialization.
Many parameters available in `json.dumps()` can be useful or even
necessary in specific situations. For example, when using an Agent with
return_intermediate_steps set to true, the output is a list of
AgentAction objects. These objects can't be serialized without using
Langchain's `dumps()` function.
The issue arises when using the Agent with a language other than
English, which may contain non-ASCII characters like 'é'. The default
behavior of `json.dumps()` sets ensure_ascii to true, converting
`{"name": "José"}` into `{"name": "Jos\u00e9"}`. This can make the
output hard to read, especially in the case of intermediate steps in
agent logs.
By allowing users to pass additional parameters to `json.dumps()` via
Langchain's dumps(), we can solve this problem. For instance, users can
set `ensure_ascii=False` to maintain the original characters.
This update also enables users to pass other useful `json.dumps()`
parameters like `sort_keys`, providing even more flexibility.
The implementation takes into account edge cases where a user might pass
a "default" parameter, which is already defined by `dumps()`, or an
"indent" parameter, which is also predefined if `pretty=True` is set.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
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@baskaryan, @eyurtsev, @hwchase17.
-->
### Description
Hello,
The [integration_test
README](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/tests)
was indicating incorrect paths for the `.env.example` and `.env` files.
`tests/.env.example` ->`tests/integration_tests/.env.example`
While it’s a minor error, it could **potentially lead to confusion** for
the document’s readers, so I’ve made the necessary corrections.
Thank you! ☺️
### Related Issue
- https://github.com/langchain-ai/langchain/pull/2806
**Description:**
Added support for a Pandas DataFrame OutputParser with format
instructions, along with unit tests and a demo notebook. Namely, we've
added the ability to request data from a DataFrame, have the LLM parse
the request, and then use that request to retrieve a well-formatted
response.
Within LangChain, it seamlessly integrates with language models like
OpenAI's `text-davinci-003`, facilitating streamlined interaction using
the format instructions (just like the other output parsers).
This parser structures its requests as
`<operation/column/row>[<optional_array_params>]`. The instructions
detail permissible operations, valid columns, and array formats,
ensuring clarity and adherence to the required format.
For example:
- When the LLM receives the input: "Retrieve the mean of `num_legs` from
rows 1 to 3."
- The provided format instructions guide the LLM to structure the
request as: "mean:num_legs[1..3]".
The parser processes this formatted request, leveraging the LLM's
understanding to extract the mean of `num_legs` from rows 1 to 3 within
the Pandas DataFrame.
This integration allows users to communicate requests naturally, with
the LLM transforming these instructions into structured commands
understood by the `PandasDataFrameOutputParser`. The format instructions
act as a bridge between natural language queries and precise DataFrame
operations, optimizing communication and data retrieval.
**Issue:**
- https://github.com/langchain-ai/langchain/issues/11532
**Dependencies:**
No additional dependencies :)
**Tag maintainer:**
@baskaryan
**Twitter handle:**
No need. :)
---------
Co-authored-by: Wasee Alam <waseealam@protonmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
When using Vald, only insecure grpc connection was supported, so secure
connection is now supported.
In addition, grpc metadata can be added to Vald requests to enable
authentication with a token.
<!-- Thank you for contributing to LangChain!
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- **Description:** a description of the change,
- **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),
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Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
grammar correction
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
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gets announced, and you'd like a mention, we'll gladly shout you out!
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tests, lint, etc:
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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.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Response_if_no_docs_found is not implemented in
ConversationalRetrievalChain for async code paths. Implemented it and
added test cases
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Description
This PR implements Self-Query Retriever for MongoDB Atlas vector store.
I've implemented the comparators and operators that are supported by
MongoDB Atlas vector store according to the section titled "Atlas Vector
Search Pre-Filter" from
https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/.
Namely:
```
allowed_comparators = [
Comparator.EQ,
Comparator.NE,
Comparator.GT,
Comparator.GTE,
Comparator.LT,
Comparator.LTE,
Comparator.IN,
Comparator.NIN,
]
"""Subset of allowed logical operators."""
allowed_operators = [
Operator.AND,
Operator.OR
]
```
Translations from comparators/operators to MongoDB Atlas filter
operators(you can find the syntax in the "Atlas Vector Search
Pre-Filter" section from the previous link) are done using the following
dictionary:
```
map_dict = {
Operator.AND: "$and",
Operator.OR: "$or",
Comparator.EQ: "$eq",
Comparator.NE: "$ne",
Comparator.GTE: "$gte",
Comparator.LTE: "$lte",
Comparator.LT: "$lt",
Comparator.GT: "$gt",
Comparator.IN: "$in",
Comparator.NIN: "$nin",
}
```
In visit_structured_query() the filters are passed as "pre_filter" and
not "filter" as in the MongoDB link above since langchain's
implementation of MongoDB atlas vector
store(libs\langchain\langchain\vectorstores\mongodb_atlas.py) in
_similarity_search_with_score() sets the "filter" key to have the value
of the "pre_filter" argument.
```
params["filter"] = pre_filter
```
Test cases and documentation have also been added.
# Issue
#11616
# Dependencies
No new dependencies have been added.
# Documentation
I have created the notebook mongodb_atlas_self_query.ipynb outlining the
steps to get the self-query mechanism working.
I worked closely with [@Farhan-Faisal](https://github.com/Farhan-Faisal)
on this PR.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Description
We implemented a simple tool for accessing the Merriam-Webster
Collegiate Dictionary API
(https://dictionaryapi.com/products/api-collegiate-dictionary).
Here's a simple usage example:
```py
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
llm = OpenAI()
tools = load_tools(["serpapi", "merriam-webster"], llm=llm) # Serp API gives our agent access to Google
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("What is the english word for the german word Himbeere? Define that word.")
```
Sample output:
```
> Entering new AgentExecutor chain...
I need to find the english word for Himbeere and then get the definition of that word.
Action: Search
Action Input: "English word for Himbeere"
Observation: {'type': 'translation_result'}
Thought: Now I have the english word, I can look up the definition.
Action: MerriamWebster
Action Input: raspberry
Observation: Definitions of 'raspberry':
1. rasp-ber-ry, noun: any of various usually black or red edible berries that are aggregate fruits consisting of numerous small drupes on a fleshy receptacle and that are usually rounder and smaller than the closely related blackberries
2. rasp-ber-ry, noun: a perennial plant (genus Rubus) of the rose family that bears raspberries
3. rasp-ber-ry, noun: a sound of contempt made by protruding the tongue between the lips and expelling air forcibly to produce a vibration; broadly : an expression of disapproval or contempt
4. black raspberry, noun: a raspberry (Rubus occidentalis) of eastern North America that has a purplish-black fruit and is the source of several cultivated varieties —called also blackcap
Thought: I now know the final answer.
Final Answer: Raspberry is an english word for Himbeere and it is defined as any of various usually black or red edible berries that are aggregate fruits consisting of numerous small drupes on a fleshy receptacle and that are usually rounder and smaller than the closely related blackberries.
> Finished chain.
```
# Issue
This closes#12039.
# Dependencies
We added no extra dependencies.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Lara <63805048+larkgz@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Update the document for drop box loader + made the
messages more verbose when loading pdf file since people were getting
confused
- **Issue:** #13952
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17,
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
