projct

Author: Erick Friis

.gitignore

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+__pycache__

Dockerfile

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+FROM python:3.11-slim
+
+RUN pip install poetry==1.6.1
+
+RUN poetry config virtualenvs.create false
+
+WORKDIR /code
+
+COPY ./pyproject.toml ./README.md ./poetry.lock* ./
+
+COPY ./package[s] ./packages
+
+RUN poetry install  --no-interaction --no-ansi --no-root
+
+COPY ./app ./app
+
+RUN poetry install --no-interaction --no-ansi
+
+EXPOSE 8080
+
+CMD exec uvicorn app.server:app --host 0.0.0.0 --port 8080

README.md

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+# perplexaty
+
+## Installation
+
+Install the LangChain CLI if you haven't yet
+
+```bash
+pip install -U langchain-cli
+```
+
+## Adding packages
+
+```bash
+# adding packages from 
+# https://github.com/langchain-ai/langchain/tree/master/templates
+langchain app add $PROJECT_NAME
+
+# adding custom GitHub repo packages
+langchain app add --repo $OWNER/$REPO
+# or with whole git string (supports other git providers):
+# langchain app add git+https://github.com/hwchase17/chain-of-verification
+
+# with a custom api mount point (defaults to `/{package_name}`)
+langchain app add $PROJECT_NAME --api_path=/my/custom/path/rag
+```
+
+Note: you remove packages by their api path
+
+```bash
+langchain app remove my/custom/path/rag
+```
+
+## Setup LangSmith (Optional)
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+## Launch LangServe
+
+```bash
+langchain serve
+```
+
+## Running in Docker
+
+This project folder includes a Dockerfile that allows you to easily build and host your LangServe app.
+
+### Building the Image
+
+To build the image, you simply:
+
+```shell
+docker build . -t my-langserve-app
+```
+
+If you tag your image with something other than `my-langserve-app`,
+note it for use in the next step.
+
+### Running the Image Locally
+
+To run the image, you'll need to include any environment variables
+necessary for your application.
+
+In the below example, we inject the `OPENAI_API_KEY` environment
+variable with the value set in my local environment
+(`$OPENAI_API_KEY`)
+
+We also expose port 8080 with the `-p 8080:8080` option.
+
+```shell
+docker run -e OPENAI_API_KEY=$OPENAI_API_KEY -p 8080:8080 my-langserve-app
+```

app/__init__.py

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+from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
+from langchain_core.runnables import (
+    RunnableLambda,
+    RunnableParallel,
+    RunnablePassthrough,
+)
+from langchain_exa import ExaSearchRetriever
+from langchain_openai import ChatOpenAI
+
+retriever = ExaSearchRetriever(k=3, highlights=True)
+
+document_prompt = PromptTemplate.from_template(
+    """
+<source>
+    <url>{url}</url>
+    <highlights>{highlights}</highlights>
+</source>
+"""
+)
+
+document_chain = (
+    RunnableLambda(
+        lambda document: {
+            "highlights": document.metadata["highlights"],
+            "url": document.metadata["url"],
+        }
+    )
+    | document_prompt
+)
+
+retrieval_chain = (
+    retriever | document_chain.map() | (lambda docs: "\n".join([i.text for i in docs]))
+)
+
+
+generation_prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "You are an expert research assistant. You use xml-formatted context to research people's questions.",
+        ),
+        (
+            "human",
+            """
+Please answer the following query based on the provided context. Please cite your sources at the end of your response.:
+     
+Query: {query}
+---
+<context>
+{context}
+</context>
+""",
+        ),
+    ]
+)
+
+llm = ChatOpenAI()
+
+chain = (
+    RunnableParallel(
+        {
+            "query": RunnablePassthrough(),
+            "context": retrieval_chain,
+        }
+    )
+    | generation_prompt
+    | llm
+).with_types(input_type=str)

app/chain.py

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+from fastapi import FastAPI
+from fastapi.responses import RedirectResponse
+from langserve import add_routes
+from app.chain import chain
+
+app = FastAPI()
+
+
+@app.get("/")
+async def redirect_root_to_docs():
+    return RedirectResponse("/docs")
+
+
+# Edit this to add the chain you want to add
+add_routes(app, chain, path="/search")
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)

app/server.py

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+[tool.poetry]
+name = "perplexaty"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <[email protected]>"]
+readme = "README.md"
+packages = [
+    { include = "app" },
+]
+
+[tool.poetry.dependencies]
+python = "^3.11"
+uvicorn = "^0.23.2"
+langserve = {extras = ["server"], version = ">=0.0.30"}
+pydantic = "<2"
+langchain-core = "^0.1.15"
+langchain-openai = "^0.0.3"
+langchain-exa = {git = "https://github.com/langchain-ai/langchain.git", rev = "erick/partner-exa", subdirectory = "libs/partners/exa"}
+
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.15"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"