This adds `input_schema` and `output_schema` properties to all
runnables, which are Pydantic models for the input and output types
respectively. These are inferred from the structure of the Runnable as
much as possible, the only manual typing needed is
- optionally add type hints to lambdas (which get translated to
input/output schemas)
- optionally add type hint to RunnablePassthrough
These schemas can then be used to create JSON Schema descriptions of
input and output types, see the tests
- [x] Ensure no InputType and OutputType in our classes use abstract
base classes (replace with union of subclasses)
- [x] Implement in BaseChain and LLMChain
- [x] Implement in RunnableBranch
- [x] Implement in RunnableBinding, RunnableMap, RunnablePassthrough,
RunnableEach, RunnableRouter
- [x] Implement in LLM, Prompt, Chat Model, Output Parser, Retriever
- [x] Implement in RunnableLambda from function signature
- [x] Implement in Tool
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Adds LangServe package
* Integrate Runnables with Fast API creating Server and a RemoteRunnable
client
* Support multiple runnables for a given server
* Support sync/async/batch/abatch/stream/astream/astream_log on the
client side (using async implementations on server)
* Adds validation using annotations (relying on pydantic under the hood)
-- this still has some rough edges -- e.g., open api docs do NOT
generate correctly at the moment
* Uses pydantic v1 namespace
Known issues: type translation code doesn't handle a lot of types (e.g.,
TypedDicts)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
The current behaviour just calls the handler without awaiting the
coroutine, which results in exceptions/warnings, and obviously doesn't
actually execute whatever the callback handler does
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- **Description:** Prompt wrapping requirements have been implemented on
the service side of AWS Bedrock for the Anthropic Claude models to
provide parity between Anthropic's offering and Bedrock's offering. This
overnight change broke most existing implementations of Claude, Bedrock
and Langchain. This PR just steals the the Anthropic LLM implementation
to enforce alias/role wrapping and implements it in the existing
mechanism for building the request body. This has also been tested to
fix the chat_model implementation as well. Happy to answer any further
questions or make changes where necessary to get things patched and up
to PyPi ASAP, TY.
- **Issue:** No issue opened at the moment, though will update when
these roll in.
- **Dependencies:** None
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description:
NotionDB supports a number of common property types. I have found three
common types that are not included in notiondb loader. When programs
loaded them with notiondb, which will cause some metadata information
not to be passed to langchain. Therefore, I added three common types:
- date
- created_time
- last_edit_time.
### Issue:
no
### Dependencies:
No dependencies added :)
### Tag maintainer:
@rlancemartin, @eyurtsev
### Twitter handle:
@BJTUTC
Reverts langchain-ai/langchain#8610
this is actually an oversight - this merges all dfs into one df. we DO
NOT want to do this - the idea is we work and manipulate multiple dfs
This removes the use of the intermediate df list and directly
concatenates the dataframes if path is a list of strings. The pd.concat
function combines the dataframes efficiently, making it faster and more
memory-efficient compared to appending dataframes to a list.
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Maintainer responsibilities:
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- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Description: this PR adds the support for arxiv identifier of the
ArxivAPIWrapper. I modified the `run()` and `load()` functions in
`arxiv.py`, using regex to recognize if the query is in the form of
arxiv identifier (see
[https://info.arxiv.org/help/find/index.html](https://info.arxiv.org/help/find/index.html)).
If so, it will directly search the paper corresponding to the arxiv
identifier. I also modified and added tests in `test_arxiv.py`.
- Issue: #9047
- Dependencies: N/A
- Tag maintainer: N/A
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The new Fireworks and FireworksChat implementations are awesome! Added
in this PR https://github.com/langchain-ai/langchain/pull/11117 thank
you @ZixinYang
However, I think stop words were not plumbed correctly. I've made some
simple changes to do that, and also updated the notebook to be a bit
clearer with what's needed to use both new models.
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
The intermediate steps example in docs has an example on how to retrieve
and display the intermediate steps.
But the intermediate steps object is of type AgentAction which cannot be
passed to json.dumps (it raises an error).
I replaced it with Langchain's dumps function (from langchain.load.dump
import dumps) which is the preferred way to do so.
**Description:**
As long as `enforce_stop_tokens` returns a first occurrence, we can
speed up the execution by setting the optional `maxsplit` parameter to
1.
Tag maintainer:
@agola11
@hwchase17
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** New metadata fields were added to
`unstructured==0.10.15`, and our hosted api has been updated to reflect
this. When users call `partition_via_api` with an older version of the
library, they'll hit a parsing error related to the new fields.
Description
* Refactor Fireworks within Langchain LLMs.
* Remove FireworksChat within Langchain LLMs.
* Add ChatFireworks (which uses chat completion api) to Langchain chat
models.
* Users have to install `fireworks-ai` and register an api key to use
the api.
Issue - Not applicable
Dependencies - None
Tag maintainer - @rlancemartin @baskaryan
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---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This enables bulk args like `chunk_size` to be passed down from the
ingest methods (from_text, from_documents) to be passed down to the bulk
API.
This helps alleviate issues where bulk importing a large amount of
documents into Elasticsearch was resulting in a timeout.
Contribution Shoutout
- @elastic
- [x] Updated Integration tests
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Fixed navbar:
- renamed several files, so ToC is sorted correctly
- made ToC items consistent: formatted several Titles
- added several links
- reformatted several docs to a consistent format
- renamed several files (removed `_example` suffix)
- added renamed files to the `docs/docs_skeleton/vercel.json`
Sometimes you don't want the LLM to be aware of the whole graph schema,
and want it to ignore parts of the graph when it is constructing Cypher
statements.
